Soft Bayesian Context Tree Models for Real-Valued Time Series
By: Shota Saito, Yuta Nakahara, Toshiyasu Matsushima
Potential Business Impact:
Predicts future numbers by learning patterns.
This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of the context space, instead of hard (deterministic) splits of the context space as in the previous BCT for real-valued time series. A learning algorithm of the Soft-BCT is proposed based on the variational inference. For some real-world datasets, the Soft-BCT demonstrates almost the same or superior performance to the previous BCT.
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